Theoretical Basis in Regression Model Based Selection of the Most Cost Effective Parameters of Hard Rock Surface Mining

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DOI: 10.4236/eng.2011.32018    4,361 Downloads   7,900 Views   Citations

ABSTRACT

What determines selection of the most cost effective parameters of hard rock surface mining is consideration of all alternative variants of mine design and the conflicting effect of their parameters on cost. Consideration could be realized based on the mathematical model of the cumulative influence of rockmass and mine design variables on the overall cost per ton of the hard rock drilled, blasted, hauled and primary crushed. Available works on the topic mostly dwelt on four processes of hard rock surface mining separately. This paper dwells on the theoretical part of a research proposed to enhance effectiveness in the selection of the parameters of hard rock surface mining design based on the regression model of overall cost per tonne of the rock mined fit on the determinant variations of rockmass and mine design. The regression model could be developed based on the statistical data generated by many of the hard rock surface mines operating in variable conditions of rockmass and mine design worldwide. Also, a regression model based general algorithm has been formulated for the development of software and computer aided selection of the most cost effective parameters of hard rock surface mining.

Cite this paper

A. Massawe, K. Baruti and P. Gongo, "Theoretical Basis in Regression Model Based Selection of the Most Cost Effective Parameters of Hard Rock Surface Mining," Engineering, Vol. 3 No. 2, 2011, pp. 156-161. doi: 10.4236/eng.2011.32018.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] W. Hustrulid and M. Kuchta, “Open Pit Mine Planning and Design,” Taylor & Francis, London, 2006.
[2] B. Kennedy, “Surface Mining,” SME, Colorado, 1990.
[3] T. Afeni, “Op-timization of Drilling and Blasting Operations in an Open Pit Mining—The Société des Mines del’ Air Experience,” Mining Science and Technology (China), Vol. 19, No. 6, 2009, pp. 736-739. doi:10.1016/S1674-52 64(09) 60134-4
[4] A. Bor-tolussi, R. Ciccu, S. Forte and B. Grosso, “A Contribution to a Better Design and Control of Surface Blasting,” Balkema, Rot-terdam, 2000.
[5] S. Qu, S. Hao, G. Chen, B. Li and G. Bian, “The Blast-Code Model—A Computer—Aided Bench Blast Design and Simulation System,” Fragblast, Vol. 6, No. 1, 2002, pp. 85-103. doi:10.1076/frag.6.1.85.8852
[6] A. Mishra, “Design of Surface Blasts—A Computational Approach,” Ba-chelor of Science Thesis, Department of Mining Engineering, National Institute of Technology, 2009.
[7] E. Bozorgrahimi, R. A. Hall and G. H. Blackwell, “Sizing Equipment for Open Pit Mining,” A Review of Critical Parameters of Mining Technology, Vol. 112, 2003, pp. 171-179.
[8] R. Franke, “Combined Mining Systems for Open Pit Mines,” Bulk Handling in Open Pit Mines & Quarries, 1986, pp. 177-182.
[9] H. Althof, “Cost Reduction by In-Pit Crushing and Conveying,” Handling in Open Pit Mines & Quarries, 1986, pp. 205-208.
[10] G. Adel, T. Kojovic and D. Thornton, “Mine-to-Mill Optimization of Aggregate Production,” Virginia Polytechnic Institute of State University, Virginia, 2006.
[11] R. Zhang, Y. Zhang, T. X. Ren and B. Denby, “The Selection of Surface Mining Technology Using a Decision Making System,” Balkema, Rotterdam, 1998.
[12] A. T. S. Massawe, “Drilling & Blasting Part I: Blasting Lecture Notes and Tutorials,” LAP LAMBERT Academic Publishing, Saarbrucken, 2010.
[13] A. T. S. Mas-sawe, “Drilling & Blasting Part II: Drilling Manual,” LAP LAMBERT Academic Publishing, Saarbrucken, 2010.

  
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